{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 写一个函数输入一张图片 返回这个图片上每个轮廓的凸点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:16.190217Z",
     "iopub.status.busy": "2021-11-16T08:48:16.190020Z",
     "iopub.status.idle": "2021-11-16T08:48:16.788477Z",
     "shell.execute_reply": "2021-11-16T08:48:16.787592Z",
     "shell.execute_reply.started": "2021-11-16T08:48:16.190193Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "程序开始运行\n",
      "程序可以正常运行\n"
     ]
    }
   ],
   "source": [
    "#导包\n",
    "print(\"程序开始运行\")\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "from matplotlib import font_manager\n",
    "\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.sans-serif'] = ['FZSongYi-Z13S'] # 指定默认字体\n",
    "mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题\n",
    "mpl.rcParams['font.size'] =20\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "print(\"程序可以正常运行\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:16.789945Z",
     "iopub.status.busy": "2021-11-16T08:48:16.789556Z",
     "iopub.status.idle": "2021-11-16T08:48:16.910713Z",
     "shell.execute_reply": "2021-11-16T08:48:16.910036Z",
     "shell.execute_reply.started": "2021-11-16T08:48:16.789919Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(203, 182, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 用imread的方法读取并显示图片\n",
    "#读取图片\n",
    "img = cv2.imread(\"m269.tif\")#img = cv2.imread(\"m809.tif\")\n",
    "print(img.shape)\n",
    "\n",
    "plt.subplot()\n",
    "# plt.axis('off')\n",
    "plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n",
    "plt.title(\"imread\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:16.912362Z",
     "iopub.status.busy": "2021-11-16T08:48:16.911800Z",
     "iopub.status.idle": "2021-11-16T08:48:17.094777Z",
     "shell.execute_reply": "2021-11-16T08:48:17.094177Z",
     "shell.execute_reply.started": "2021-11-16T08:48:16.912333Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "灰度图的数据类型: <class 'numpy.ndarray'> \t灰度图的维度: (203, 182)\n"
     ]
    }
   ],
   "source": [
    "img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "plt.subplot()\n",
    "# plt.axis('off')\n",
    "plt.imshow(cv2.cvtColor(img_gray, cv2.COLOR_BGR2RGB))\n",
    "plt.title(\"灰度图\")\n",
    "plt.show()\n",
    "print(\"灰度图的数据类型:\",type(img_gray),\"\\t灰度图的维度:\",img_gray.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.097104Z",
     "iopub.status.busy": "2021-11-16T08:48:17.096432Z",
     "iopub.status.idle": "2021-11-16T08:48:17.275417Z",
     "shell.execute_reply": "2021-11-16T08:48:17.239633Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.097076Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "二值图的数据类型: <class 'numpy.ndarray'> \t二值图的维度: (203, 182)\n"
     ]
    }
   ],
   "source": [
    "#二值化   找到轮廓就像从黑色背景中找到白色物体。因此请记住，要找到的对象应该是白色，背景应该是黑色\n",
    "ret,thresh = cv2.threshold(img_gray, 254, 255,1)#灰度图的背景是白色 要把背景变成黑色 石头变成白色 所以这里通过这种方法二值化\n",
    "\n",
    "plt.subplot()\n",
    "# plt.axis('off')\n",
    "plt.imshow(cv2.cvtColor(thresh, cv2.COLOR_BGR2RGB))\n",
    "plt.title(\"二值图\")\n",
    "plt.show()\n",
    "print(\"二值图的数据类型:\",type(thresh),\"\\t二值图的维度:\",thresh.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.277536Z",
     "iopub.status.busy": "2021-11-16T08:48:17.276875Z",
     "iopub.status.idle": "2021-11-16T08:48:17.281860Z",
     "shell.execute_reply": "2021-11-16T08:48:17.281329Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.277495Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "找到图片的轮廓个数: 1\n"
     ]
    }
   ],
   "source": [
    "contours,hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)#cv2.RETR_EXTERNAL 只检测外轮廓    cv2.CHAIN_APPROX_NONE 存储所有边界点 \n",
    "print(\"找到图片的轮廓个数:\",len(contours))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.283300Z",
     "iopub.status.busy": "2021-11-16T08:48:17.282699Z",
     "iopub.status.idle": "2021-11-16T08:48:17.286757Z",
     "shell.execute_reply": "2021-11-16T08:48:17.286249Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.283275Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "当前轮廓里面点的个数: 800\n"
     ]
    }
   ],
   "source": [
    "for cnt in contours:\n",
    "    print(\"当前轮廓里面点的个数:\",len(cnt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.288065Z",
     "iopub.status.busy": "2021-11-16T08:48:17.287549Z",
     "iopub.status.idle": "2021-11-16T08:48:17.292709Z",
     "shell.execute_reply": "2021-11-16T08:48:17.292147Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.288042Z"
    }
   },
   "outputs": [],
   "source": [
    "#写一个函数 输入一个cnt 返回这个cnt轮廓上面的凸点坐标 放在一个大的list里面\n",
    "def findConvexHull(cnt):\n",
    "    \"\"\"输入一个cnt 返回这个cnt上面所有的凸点坐标\"\"\"\n",
    "    cnt=cnt.reshape([cnt.shape[0],cnt.shape[-1]])\n",
    "    \n",
    "    hull_index=cv2.convexHull(cnt,returnPoints = False)#returnPoints=False 表示找到的是地址 \n",
    "    #hull_index此时是一个(n,1)的二维数组  就是有n个元素 每个元素是一个[数字]  这个数字代表凸点在轮廓cnt中的索引\n",
    "    #通过轮廓cnt 和索引 hull_index 找到凸点的坐标即可\n",
    "    \n",
    "    convexPoint=cnt[hull_index[:,0]]\n",
    "    return convexPoint#此时返回的凸点坐标是一个二维数组  里面有n个 元素 每个元素一个坐标（x,y）形成的数组\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.294001Z",
     "iopub.status.busy": "2021-11-16T08:48:17.293609Z",
     "iopub.status.idle": "2021-11-16T08:48:17.300227Z",
     "shell.execute_reply": "2021-11-16T08:48:17.299502Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.293979Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([180,  60], dtype=int32), array([174, 168], dtype=int32), array([168, 173], dtype=int32), array([166, 174], dtype=int32), array([ 55, 202], dtype=int32), array([ 14, 202], dtype=int32), array([ 12, 200], dtype=int32), array([  8, 195], dtype=int32), array([  6, 187], dtype=int32), array([  1, 160], dtype=int32), array([  1, 152], dtype=int32), array([  2, 138], dtype=int32), array([  5, 124], dtype=int32), array([  6, 120], dtype=int32), array([  7, 117], dtype=int32), array([  8, 115], dtype=int32), array([36, 63], dtype=int32), array([56, 34], dtype=int32), array([60, 30], dtype=int32), array([77, 17], dtype=int32), array([89, 10], dtype=int32), array([102,   3], dtype=int32), array([106,   1], dtype=int32), array([130,   1], dtype=int32), array([157,   8], dtype=int32), array([160,  11], dtype=int32), array([178,  33], dtype=int32), array([180,  36], dtype=int32)]\n"
     ]
    }
   ],
   "source": [
    "#先来找一下这个原图的二值图的轮廓凸点\n",
    "m269convexPoint=[]\n",
    "for cnt in contours:\n",
    "    m269convexPoint.extend(findConvexHull(cnt))\n",
    "print(m269convexPoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.301572Z",
     "iopub.status.busy": "2021-11-16T08:48:17.301394Z",
     "iopub.status.idle": "2021-11-16T08:48:17.316188Z",
     "shell.execute_reply": "2021-11-16T08:48:17.315523Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.301542Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([180,  60], dtype=int32),\n",
       " array([174, 168], dtype=int32),\n",
       " array([168, 173], dtype=int32),\n",
       " array([166, 174], dtype=int32),\n",
       " array([ 55, 202], dtype=int32),\n",
       " array([ 14, 202], dtype=int32),\n",
       " array([ 12, 200], dtype=int32),\n",
       " array([  8, 195], dtype=int32),\n",
       " array([  6, 187], dtype=int32),\n",
       " array([  1, 160], dtype=int32),\n",
       " array([  1, 152], dtype=int32),\n",
       " array([  2, 138], dtype=int32),\n",
       " array([  5, 124], dtype=int32),\n",
       " array([  6, 120], dtype=int32),\n",
       " array([  7, 117], dtype=int32),\n",
       " array([  8, 115], dtype=int32),\n",
       " array([36, 63], dtype=int32),\n",
       " array([56, 34], dtype=int32),\n",
       " array([60, 30], dtype=int32),\n",
       " array([77, 17], dtype=int32),\n",
       " array([89, 10], dtype=int32),\n",
       " array([102,   3], dtype=int32),\n",
       " array([106,   1], dtype=int32),\n",
       " array([130,   1], dtype=int32),\n",
       " array([157,   8], dtype=int32),\n",
       " array([160,  11], dtype=int32),\n",
       " array([178,  33], dtype=int32),\n",
       " array([180,  36], dtype=int32)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m269convexPoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.317739Z",
     "iopub.status.busy": "2021-11-16T08:48:17.317252Z",
     "iopub.status.idle": "2021-11-16T08:48:17.321757Z",
     "shell.execute_reply": "2021-11-16T08:48:17.321257Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.317707Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(m269convexPoint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 函数写好了 来运用在需要找轮廓的点的图上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.322676Z",
     "iopub.status.busy": "2021-11-16T08:48:17.322481Z",
     "iopub.status.idle": "2021-11-16T08:48:17.380826Z",
     "shell.execute_reply": "2021-11-16T08:48:17.379608Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.322629Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[180,  60],\n",
       "       [174, 168],\n",
       "       [168, 173],\n",
       "       [166, 174],\n",
       "       [ 55, 202],\n",
       "       [ 14, 202],\n",
       "       [ 12, 200],\n",
       "       [  8, 195],\n",
       "       [  6, 187],\n",
       "       [  1, 160],\n",
       "       [  1, 152],\n",
       "       [  2, 138],\n",
       "       [  5, 124],\n",
       "       [  6, 120],\n",
       "       [  7, 117],\n",
       "       [  8, 115],\n",
       "       [ 36,  63],\n",
       "       [ 56,  34],\n",
       "       [ 60,  30],\n",
       "       [ 77,  17],\n",
       "       [ 89,  10],\n",
       "       [102,   3],\n",
       "       [106,   1],\n",
       "       [130,   1],\n",
       "       [157,   8],\n",
       "       [160,  11],\n",
       "       [178,  33],\n",
       "       [180,  36]], dtype=int32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m269convexPointArray=np.array(m269convexPoint)\n",
    "m269convexPointArray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.382822Z",
     "iopub.status.busy": "2021-11-16T08:48:17.382497Z",
     "iopub.status.idle": "2021-11-16T08:48:17.389405Z",
     "shell.execute_reply": "2021-11-16T08:48:17.388598Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.382780Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([180, 174, 168, 166,  55,  14,  12,   8,   6,   1,   1,   2,   5,\n",
       "         6,   7,   8,  36,  56,  60,  77,  89, 102, 106, 130, 157, 160,\n",
       "       178, 180], dtype=int32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m269convexPointArray[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.395480Z",
     "iopub.status.busy": "2021-11-16T08:48:17.394582Z",
     "iopub.status.idle": "2021-11-16T08:48:17.400684Z",
     "shell.execute_reply": "2021-11-16T08:48:17.399990Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.395437Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 60, 168, 173, 174, 202, 202, 200, 195, 187, 160, 152, 138, 124,\n",
       "       120, 117, 115,  63,  34,  30,  17,  10,   3,   1,   1,   8,  11,\n",
       "        33,  36], dtype=int32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m269convexPointArray[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.402664Z",
     "iopub.status.busy": "2021-11-16T08:48:17.402040Z",
     "iopub.status.idle": "2021-11-16T08:48:17.407703Z",
     "shell.execute_reply": "2021-11-16T08:48:17.406912Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.402606Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([180,  60], dtype=int32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m269convexPointArray[:][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.409549Z",
     "iopub.status.busy": "2021-11-16T08:48:17.408921Z",
     "iopub.status.idle": "2021-11-16T08:48:17.526824Z",
     "shell.execute_reply": "2021-11-16T08:48:17.526277Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.409508Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "二值图的数据类型: <class 'numpy.ndarray'> \t二值图的维度: (203, 182)\n"
     ]
    }
   ],
   "source": [
    "plt.subplot()\n",
    "# plt.axis('off')\n",
    "plt.imshow(cv2.cvtColor(thresh, cv2.COLOR_BGR2RGB))\n",
    "plt.scatter(m269convexPointArray[:,0],m269convexPointArray[:,1],c='red',s=1,label=\"凸包点\")\n",
    "plt.title(\"二值图\")\n",
    "plt.show()\n",
    "print(\"二值图的数据类型:\",type(thresh),\"\\t二值图的维度:\",thresh.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 运用在原来的图上是可以的 现在来找原图上要找的凹点到边界直线距离大于阈值哪个封闭区域中的凸点 然后排除距离原图像凸点太近的点\n",
    "### 首先找到原图像的凸点和哪个封闭轮廓  然后画出来 二值化一下 用一下上面哪个函数即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.527888Z",
     "iopub.status.busy": "2021-11-16T08:48:17.527691Z",
     "iopub.status.idle": "2021-11-16T08:48:17.531487Z",
     "shell.execute_reply": "2021-11-16T08:48:17.530831Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.527865Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#距离阈值\n",
    "distance_Threshold=2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.532659Z",
     "iopub.status.busy": "2021-11-16T08:48:17.532342Z",
     "iopub.status.idle": "2021-11-16T08:48:17.578147Z",
     "shell.execute_reply": "2021-11-16T08:48:17.577391Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.532637Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cnt: [[[106   1]]\n",
      "\n",
      " [[106   2]]\n",
      "\n",
      " [[105   3]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[109   1]]\n",
      "\n",
      " [[108   1]]\n",
      "\n",
      " [[107   1]]] <class 'numpy.ndarray'>\n",
      "hull_index: [[694]\n",
      " [533]\n",
      " [527]\n",
      " [525]\n",
      " [388]\n",
      " [347]\n",
      " [345]\n",
      " [339]\n",
      " [331]\n",
      " [304]\n",
      " [296]\n",
      " [282]\n",
      " [268]\n",
      " [264]\n",
      " [261]\n",
      " [259]\n",
      " [ 82]\n",
      " [ 52]\n",
      " [ 48]\n",
      " [ 30]\n",
      " [ 18]\n",
      " [  5]\n",
      " [  0]\n",
      " [776]\n",
      " [749]\n",
      " [746]\n",
      " [721]\n",
      " [718]] <class 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[array([[   82,   259,   158, 18847],\n",
       "        [  388,   525,   441,  7431],\n",
       "        [  533,   694,   622, 16302]], dtype=int32)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#把轮廓和凸包的索引给喂进去  然后就可以得到了defects\n",
    "defects_target_list=[]\n",
    "for cnt in contours:\n",
    "    print('cnt:',cnt,type(cnt))\n",
    "    hull_index=cv2.convexHull(cnt,returnPoints = False)\n",
    "    print('hull_index:',hull_index,type(hull_index))\n",
    "    defects = cv2.convexityDefects(cnt,hull_index)\n",
    "    defects=defects.reshape(defects.shape[0],defects.shape[-1])\n",
    "    #筛选数组里面距离大于阈值的数据  defects是里面是一个4个元素的数组  分别是轮廓起点 终点  距离最大值点 和距离 所以按照索引第三个元素的值大于距离阈值即可\n",
    "    defects_target=defects[defects[:,3]>distance_Threshold]\n",
    "    defects_target_list.append(defects_target)\n",
    "\n",
    "defects_target_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.579541Z",
     "iopub.status.busy": "2021-11-16T08:48:17.579184Z",
     "iopub.status.idle": "2021-11-16T08:48:17.583398Z",
     "shell.execute_reply": "2021-11-16T08:48:17.582850Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.579517Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[   82,   259,   158, 18847],\n",
       "        [  388,   525,   441,  7431],\n",
       "        [  533,   694,   622, 16302]], dtype=int32)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "defects_target_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.584562Z",
     "iopub.status.busy": "2021-11-16T08:48:17.584183Z",
     "iopub.status.idle": "2021-11-16T08:48:17.590024Z",
     "shell.execute_reply": "2021-11-16T08:48:17.589257Z",
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       "       [  388,   525,   441,  7431],\n",
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    "point1=[]\n",
    "for i in range(defects_target_list[0][0][0],defects_target_list[0][0][1]):\n",
    "    point1.append(tuple(cnt[i][0]))\n",
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    "point2=[]\n",
    "for i in range(defects_target_list[0][1][0],defects_target_list[0][1][1]):\n",
    "    point2.append(tuple(cnt[i][0]))\n",
    "point2"
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     "execution_count": 25,
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   "source": [
    "point3=[]\n",
    "for i in range(defects_target_list[0][2][0],defects_target_list[0][2][1]):\n",
    "    point3.append(tuple(cnt[i][0]))\n",
    "point3"
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  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
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    {
     "data": {
      "text/plain": [
       "(36, 63)"
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     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "tuple(cnt[82][0])"
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     "iopub.status.idle": "2021-11-16T08:48:17.703578Z",
     "shell.execute_reply": "2021-11-16T08:48:17.703060Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.699626Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[36, 63]], dtype=int32)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnt[82]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.705044Z",
     "iopub.status.busy": "2021-11-16T08:48:17.704361Z",
     "iopub.status.idle": "2021-11-16T08:48:17.708794Z",
     "shell.execute_reply": "2021-11-16T08:48:17.708328Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.705019Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([36, 63], dtype=int32),)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple(cnt[82])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.709973Z",
     "iopub.status.busy": "2021-11-16T08:48:17.709519Z",
     "iopub.status.idle": "2021-11-16T08:48:17.713650Z",
     "shell.execute_reply": "2021-11-16T08:48:17.713202Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.709949Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  8, 115]], dtype=int32)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnt[259]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.715010Z",
     "iopub.status.busy": "2021-11-16T08:48:17.714368Z",
     "iopub.status.idle": "2021-11-16T08:48:17.740244Z",
     "shell.execute_reply": "2021-11-16T08:48:17.739694Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.714984Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(36, 63) (36, 64)\n",
      "(36, 64) (35, 65)\n",
      "(35, 65) (35, 66)\n",
      "(35, 66) (35, 67)\n",
      "(35, 67) (35, 68)\n",
      "(35, 68) (35, 69)\n",
      "(35, 69) (35, 70)\n",
      "(35, 70) (35, 71)\n",
      "(35, 71) (35, 72)\n",
      "(35, 72) (35, 73)\n",
      "(35, 73) (35, 74)\n",
      "(35, 74) (36, 75)\n",
      "(36, 75) (36, 76)\n",
      "(36, 76) (37, 77)\n",
      "(37, 77) (38, 78)\n",
      "(38, 78) (39, 79)\n",
      "(39, 79) (40, 80)\n",
      "(40, 80) (41, 81)\n",
      "(41, 81) (42, 81)\n",
      "(42, 81) (43, 82)\n",
      "(43, 82) (44, 82)\n",
      "(44, 82) (45, 83)\n",
      "(45, 83) (46, 83)\n",
      "(46, 83) (47, 84)\n",
      "(47, 84) (48, 85)\n",
      "(48, 85) (49, 85)\n",
      "(49, 85) (50, 85)\n",
      "(50, 85) (51, 86)\n",
      "(51, 86) (52, 86)\n",
      "(52, 86) (53, 86)\n",
      "(53, 86) (54, 86)\n",
      "(54, 86) (55, 87)\n",
      "(55, 87) (55, 88)\n",
      "(55, 88) (56, 88)\n",
      "(56, 88) (57, 88)\n",
      "(57, 88) (58, 89)\n",
      "(58, 89) (59, 90)\n",
      "(59, 90) (60, 90)\n",
      "(60, 90) (61, 91)\n",
      "(61, 91) (62, 91)\n",
      "(62, 91) (63, 91)\n",
      "(63, 91) (64, 91)\n",
      "(64, 91) (65, 92)\n",
      "(65, 92) (66, 93)\n",
      "(66, 93) (67, 93)\n",
      "(67, 93) (68, 93)\n",
      "(68, 93) (69, 93)\n",
      "(69, 93) (70, 93)\n",
      "(70, 93) (71, 94)\n",
      "(71, 94) (72, 94)\n",
      "(72, 94) (73, 94)\n",
      "(73, 94) (74, 95)\n",
      "(74, 95) (75, 96)\n",
      "(75, 96) (76, 96)\n",
      "(76, 96) (77, 96)\n",
      "(77, 96) (78, 96)\n",
      "(78, 96) (79, 97)\n",
      "(79, 97) (80, 98)\n",
      "(80, 98) (81, 98)\n",
      "(81, 98) (82, 98)\n",
      "(82, 98) (83, 99)\n",
      "(83, 99) (84, 99)\n",
      "(84, 99) (85, 99)\n",
      "(85, 99) (86, 100)\n",
      "(86, 100) (87, 100)\n",
      "(87, 100) (88, 101)\n",
      "(88, 101) (89, 102)\n",
      "(89, 102) (90, 102)\n",
      "(90, 102) (91, 102)\n",
      "(91, 102) (92, 102)\n",
      "(92, 102) (93, 102)\n",
      "(93, 102) (94, 102)\n",
      "(94, 102) (95, 103)\n",
      "(95, 103) (96, 103)\n",
      "(96, 103) (97, 104)\n",
      "(97, 104) (97, 105)\n",
      "(97, 105) (96, 106)\n",
      "(96, 106) (95, 107)\n",
      "(95, 107) (94, 108)\n",
      "(94, 108) (93, 108)\n",
      "(93, 108) (92, 109)\n",
      "(92, 109) (91, 110)\n",
      "(91, 110) (91, 111)\n",
      "(91, 111) (90, 112)\n",
      "(90, 112) (89, 112)\n",
      "(89, 112) (88, 113)\n",
      "(88, 113) (87, 113)\n",
      "(87, 113) (86, 114)\n",
      "(86, 114) (85, 115)\n",
      "(85, 115) (84, 115)\n",
      "(84, 115) (83, 116)\n",
      "(83, 116) (82, 117)\n",
      "(82, 117) (81, 118)\n",
      "(81, 118) (81, 119)\n",
      "(81, 119) (80, 120)\n",
      "(80, 120) (79, 121)\n",
      "(79, 121) (79, 122)\n",
      "(79, 122) (79, 123)\n",
      "(79, 123) (78, 124)\n",
      "(78, 124) (78, 125)\n",
      "(78, 125) (78, 126)\n",
      "(78, 126) (77, 127)\n",
      "(77, 127) (76, 128)\n",
      "(76, 128) (76, 129)\n",
      "(76, 129) (75, 130)\n",
      "(75, 130) (75, 131)\n",
      "(75, 131) (74, 132)\n",
      "(74, 132) (73, 133)\n",
      "(73, 133) (72, 134)\n",
      "(72, 134) (71, 134)\n",
      "(71, 134) (70, 134)\n",
      "(70, 134) (69, 133)\n",
      "(69, 133) (68, 132)\n",
      "(68, 132) (67, 131)\n",
      "(67, 131) (66, 131)\n",
      "(66, 131) (65, 131)\n",
      "(65, 131) (64, 130)\n",
      "(64, 130) (64, 129)\n",
      "(64, 129) (63, 128)\n",
      "(63, 128) (62, 127)\n",
      "(62, 127) (62, 126)\n",
      "(62, 126) (61, 126)\n",
      "(61, 126) (60, 126)\n",
      "(60, 126) (59, 125)\n",
      "(59, 125) (59, 124)\n",
      "(59, 124) (58, 123)\n",
      "(58, 123) (57, 123)\n",
      "(57, 123) (56, 123)\n",
      "(56, 123) (55, 122)\n",
      "(55, 122) (55, 121)\n",
      "(55, 121) (54, 120)\n",
      "(54, 120) (53, 120)\n",
      "(53, 120) (52, 120)\n",
      "(52, 120) (51, 119)\n",
      "(51, 119) (50, 119)\n",
      "(50, 119) (49, 118)\n",
      "(49, 118) (48, 118)\n",
      "(48, 118) (47, 118)\n",
      "(47, 118) (46, 118)\n",
      "(46, 118) (45, 117)\n",
      "(45, 117) (44, 116)\n",
      "(44, 116) (43, 116)\n",
      "(43, 116) (42, 116)\n",
      "(42, 116) (41, 115)\n",
      "(41, 115) (40, 115)\n",
      "(40, 115) (39, 115)\n",
      "(39, 115) (38, 114)\n",
      "(38, 114) (37, 114)\n",
      "(37, 114) (36, 113)\n",
      "(36, 113) (35, 113)\n",
      "(35, 113) (34, 113)\n",
      "(34, 113) (33, 112)\n",
      "(33, 112) (32, 112)\n",
      "(32, 112) (31, 111)\n",
      "(31, 111) (30, 111)\n",
      "(30, 111) (29, 111)\n",
      "(29, 111) (28, 111)\n",
      "(28, 111) (27, 110)\n",
      "(27, 110) (26, 110)\n",
      "(26, 110) (25, 110)\n",
      "(25, 110) (24, 110)\n",
      "(24, 110) (23, 110)\n",
      "(23, 110) (22, 110)\n",
      "(22, 110) (21, 110)\n",
      "(21, 110) (20, 110)\n",
      "(20, 110) (19, 110)\n",
      "(19, 110) (18, 110)\n",
      "(18, 110) (17, 110)\n",
      "(17, 110) (16, 110)\n",
      "(16, 110) (15, 111)\n",
      "(15, 111) (14, 111)\n",
      "(14, 111) (13, 111)\n",
      "(13, 111) (12, 112)\n",
      "(12, 112) (11, 113)\n",
      "(11, 113) (10, 113)\n",
      "(10, 113) (9, 114)\n"
     ]
    }
   ],
   "source": [
    "#有了这个数组 就可以在灰度图上把线和轮廓画出来了\n",
    "for k in range(len(point1)-1):\n",
    "    print(point1[k],point1[k+1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.741414Z",
     "iopub.status.busy": "2021-11-16T08:48:17.741039Z",
     "iopub.status.idle": "2021-11-16T08:48:17.745048Z",
     "shell.execute_reply": "2021-11-16T08:48:17.744546Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.741389Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36, 63)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point1[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.746270Z",
     "iopub.status.busy": "2021-11-16T08:48:17.745766Z",
     "iopub.status.idle": "2021-11-16T08:48:17.749602Z",
     "shell.execute_reply": "2021-11-16T08:48:17.749161Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.746247Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 114)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point1[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.750616Z",
     "iopub.status.busy": "2021-11-16T08:48:17.750320Z",
     "iopub.status.idle": "2021-11-16T08:48:17.897468Z",
     "shell.execute_reply": "2021-11-16T08:48:17.896667Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.750593Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "灰度图的数据类型: <class 'numpy.ndarray'> \t灰度图的维度: (203, 182)\n"
     ]
    }
   ],
   "source": [
    "cv2.line(img_gray,point1[0],point1[-1],(0,0,0),1)\n",
    "cv2.line(img_gray,point2[0],point2[-1],(0,0,0),1)\n",
    "cv2.line(img_gray,point3[0],point3[-1],(0,0,0),1)\n",
    "\n",
    "for k in range(len(point1)-1):\n",
    "    cv2.line(img_gray,point1[k],point1[k+1],(0,0,0),1)\n",
    "    \n",
    "for k in range(len(point2)-1):\n",
    "    cv2.line(img_gray,point2[k],point2[k+1],(0,0,0),1)\n",
    "\n",
    "for k in range(len(point3)-1):\n",
    "    cv2.line(img_gray,point3[k],point3[k+1],(0,0,0),1)   \n",
    "    \n",
    "plt.subplot()\n",
    "# plt.axis('off')\n",
    "plt.imshow(cv2.cvtColor(img_gray, cv2.COLOR_BGR2RGB))\n",
    "plt.title(\"灰度图\")\n",
    "plt.show()\n",
    "print(\"灰度图的数据类型:\",type(img_gray),\"\\t灰度图的维度:\",img_gray.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:17.898821Z",
     "iopub.status.busy": "2021-11-16T08:48:17.898574Z",
     "iopub.status.idle": "2021-11-16T08:48:18.091712Z",
     "shell.execute_reply": "2021-11-16T08:48:18.090452Z",
     "shell.execute_reply.started": "2021-11-16T08:48:17.898795Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "灰度图的数据类型: <class 'numpy.ndarray'> \t灰度图的维度: (203, 182)\n"
     ]
    }
   ],
   "source": [
    "plt.subplot()\n",
    "# plt.axis('off')\n",
    "plt.imshow(cv2.cvtColor(img_gray, cv2.COLOR_BGR2RGB))\n",
    "plt.title(\"灰度图\")\n",
    "plt.show()\n",
    "print(\"灰度图的数据类型:\",type(img_gray),\"\\t灰度图的维度:\",img_gray.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:18.093861Z",
     "iopub.status.busy": "2021-11-16T08:48:18.093347Z",
     "iopub.status.idle": "2021-11-16T08:48:18.277305Z",
     "shell.execute_reply": "2021-11-16T08:48:18.235740Z",
     "shell.execute_reply.started": "2021-11-16T08:48:18.093814Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "灰度图二值图的数据类型: <class 'numpy.ndarray'> \t灰度图二值图的维度: (203, 182)\n"
     ]
    }
   ],
   "source": [
    "#再把这个 img_gray 进行一下二值化  如果是像素为0 就是纯黑色的话 就把他变白  否则就变黑\n",
    "img_gray_ret,img_gray_thresh = cv2.threshold(img_gray, 0, 255,1)#灰度图的背景是白色 要把背景变成黑色 石头变成白色 所以这里通过这种方法二值化\n",
    "\n",
    "plt.subplot()\n",
    "# plt.axis('off')\n",
    "plt.imshow(cv2.cvtColor(img_gray_thresh, cv2.COLOR_BGR2RGB))\n",
    "plt.title(\"灰度图二值图\")\n",
    "plt.show()\n",
    "print(\"灰度图二值图的数据类型:\",type(img_gray_thresh),\"\\t灰度图二值图的维度:\",img_gray_thresh.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:18.281094Z",
     "iopub.status.busy": "2021-11-16T08:48:18.280289Z",
     "iopub.status.idle": "2021-11-16T08:48:18.454541Z",
     "shell.execute_reply": "2021-11-16T08:48:18.453856Z",
     "shell.execute_reply.started": "2021-11-16T08:48:18.281052Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "灰度图二值图的数据类型: <class 'numpy.ndarray'> \t灰度图二值图的维度: (203, 182)\n"
     ]
    }
   ],
   "source": [
    "plt.subplot()\n",
    "plt.axis('off')\n",
    "plt.imshow(cv2.cvtColor(img_gray_thresh, cv2.COLOR_BGR2RGB))\n",
    "plt.title(\"灰度图二值图\")\n",
    "plt.show()\n",
    "print(\"灰度图二值图的数据类型:\",type(img_gray_thresh),\"\\t灰度图二值图的维度:\",img_gray_thresh.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:18.456651Z",
     "iopub.status.busy": "2021-11-16T08:48:18.455883Z",
     "iopub.status.idle": "2021-11-16T08:48:18.462086Z",
     "shell.execute_reply": "2021-11-16T08:48:18.461435Z",
     "shell.execute_reply.started": "2021-11-16T08:48:18.456612Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_gray_contours,img_gray_hierarchy = cv2.findContours(img_gray_thresh, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)#cv2.RETR_EXTERNAL 只检测外轮廓    cv2.CHAIN_APPROX_NONE 存储所有边界点 \n",
    "len(img_gray_contours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:18.464039Z",
     "iopub.status.busy": "2021-11-16T08:48:18.463180Z",
     "iopub.status.idle": "2021-11-16T08:48:18.480793Z",
     "shell.execute_reply": "2021-11-16T08:48:18.480176Z",
     "shell.execute_reply.started": "2021-11-16T08:48:18.464002Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([165, 174], dtype=int32),\n",
       " array([163, 175], dtype=int32),\n",
       " array([108, 189], dtype=int32),\n",
       " array([ 56, 202], dtype=int32),\n",
       " array([ 55, 202], dtype=int32),\n",
       " array([ 80, 168], dtype=int32),\n",
       " array([ 83, 165], dtype=int32),\n",
       " array([118, 161], dtype=int32),\n",
       " array([123, 161], dtype=int32),\n",
       " array([132, 163], dtype=int32),\n",
       " array([ 97, 105], dtype=int32),\n",
       " array([ 75, 131], dtype=int32),\n",
       " array([ 72, 134], dtype=int32),\n",
       " array([ 70, 134], dtype=int32),\n",
       " array([  9, 114], dtype=int32),\n",
       " array([17, 98], dtype=int32),\n",
       " array([35, 64], dtype=int32),\n",
       " array([36, 63], dtype=int32),\n",
       " array([ 96, 103], dtype=int32),\n",
       " array([ 97, 104], dtype=int32),\n",
       " array([179,  71], dtype=int32),\n",
       " array([178,  93], dtype=int32),\n",
       " array([175, 157], dtype=int32),\n",
       " array([174, 168], dtype=int32),\n",
       " array([114, 100], dtype=int32),\n",
       " array([117,  97], dtype=int32),\n",
       " array([179,  61], dtype=int32)]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m269imgGrayconvexPoint=[]\n",
    "for cnt in img_gray_contours:\n",
    "    m269imgGrayconvexPoint.extend(findConvexHull(cnt))\n",
    "m269imgGrayconvexPoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:18.482277Z",
     "iopub.status.busy": "2021-11-16T08:48:18.481835Z",
     "iopub.status.idle": "2021-11-16T08:48:18.494427Z",
     "shell.execute_reply": "2021-11-16T08:48:18.493650Z",
     "shell.execute_reply.started": "2021-11-16T08:48:18.482253Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[165 174] <class 'numpy.ndarray'> (2,) (165, 174)\n",
      "[163 175] <class 'numpy.ndarray'> (2,) (163, 175)\n",
      "[108 189] <class 'numpy.ndarray'> (2,) (108, 189)\n",
      "[ 56 202] <class 'numpy.ndarray'> (2,) (56, 202)\n",
      "[ 55 202] <class 'numpy.ndarray'> (2,) (55, 202)\n",
      "[ 80 168] <class 'numpy.ndarray'> (2,) (80, 168)\n",
      "[ 83 165] <class 'numpy.ndarray'> (2,) (83, 165)\n",
      "[118 161] <class 'numpy.ndarray'> (2,) (118, 161)\n",
      "[123 161] <class 'numpy.ndarray'> (2,) (123, 161)\n",
      "[132 163] <class 'numpy.ndarray'> (2,) (132, 163)\n",
      "[ 97 105] <class 'numpy.ndarray'> (2,) (97, 105)\n",
      "[ 75 131] <class 'numpy.ndarray'> (2,) (75, 131)\n",
      "[ 72 134] <class 'numpy.ndarray'> (2,) (72, 134)\n",
      "[ 70 134] <class 'numpy.ndarray'> (2,) (70, 134)\n",
      "[  9 114] <class 'numpy.ndarray'> (2,) (9, 114)\n",
      "[17 98] <class 'numpy.ndarray'> (2,) (17, 98)\n",
      "[35 64] <class 'numpy.ndarray'> (2,) (35, 64)\n",
      "[36 63] <class 'numpy.ndarray'> (2,) (36, 63)\n",
      "[ 96 103] <class 'numpy.ndarray'> (2,) (96, 103)\n",
      "[ 97 104] <class 'numpy.ndarray'> (2,) (97, 104)\n",
      "[179  71] <class 'numpy.ndarray'> (2,) (179, 71)\n",
      "[178  93] <class 'numpy.ndarray'> (2,) (178, 93)\n",
      "[175 157] <class 'numpy.ndarray'> (2,) (175, 157)\n",
      "[174 168] <class 'numpy.ndarray'> (2,) (174, 168)\n",
      "[114 100] <class 'numpy.ndarray'> (2,) (114, 100)\n",
      "[117  97] <class 'numpy.ndarray'> (2,) (117, 97)\n",
      "[179  61] <class 'numpy.ndarray'> (2,) (179, 61)\n"
     ]
    }
   ],
   "source": [
    "for i in m269imgGrayconvexPoint:\n",
    "    cv2.circle(img_gray,tuple(i),2,[0,255,255],-1)\n",
    "    print(i,type(i),i.shape,tuple(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:18.499635Z",
     "iopub.status.busy": "2021-11-16T08:48:18.499251Z",
     "iopub.status.idle": "2021-11-16T08:48:18.504079Z",
     "shell.execute_reply": "2021-11-16T08:48:18.503324Z",
     "shell.execute_reply.started": "2021-11-16T08:48:18.499610Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([165, 174], dtype=int32)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m269imgGrayconvexPoint[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T08:48:18.505347Z",
     "iopub.status.busy": "2021-11-16T08:48:18.505137Z",
     "iopub.status.idle": "2021-11-16T08:48:18.698972Z",
     "shell.execute_reply": "2021-11-16T08:48:18.698077Z",
     "shell.execute_reply.started": "2021-11-16T08:48:18.505323Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "灰度图二值图的数据类型: <class 'numpy.ndarray'> \t灰度图二值图的维度: (203, 182)\n"
     ]
    }
   ],
   "source": [
    "plt.subplot()\n",
    "# plt.axis('off')\n",
    "\n",
    "plt.imshow(cv2.cvtColor(img_gray, cv2.COLOR_BGR2RGB))\n",
    "plt.title(\"灰度图二值图\")\n",
    "plt.show()\n",
    "print(\"灰度图二值图的数据类型:\",type(img),\"\\t灰度图二值图的维度:\",img_gray_thresh.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "py35-paddle1.2.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
